Pattern forecasts usually include mathematical forecasts for forecast objects based on consumption series, i.e., histories from the past for the particular forecast objects which may be compiled on the basis of patterns.
Previous methods for generating forecasts use only the consumption series for the particular object for which the forecast is to be generated. Through novel methods, fluctuations in the history may be compensated or smoothed directly in generating the forecast.
Forecast methods from the literature include:                the method for exponential smoothing of the first order,        the method for exponential smoothing of the second order,        the method for exponential smoothing of the third-order (according to Winters),        the method of the sliding average, and        regression analysis.        
The history of the object for which the forecast is to be generated is used in creating the forecast.